An intelligent system for prioritisation of organ transplant patient waiting lists using fuzzy logic

The objective of this paper is to investigate the effectiveness of using fuzzy logic in a complex decision-making capacity, and in particular, for the prioritisation of kidney transplant recipients. Fuzzy logic is an extension to Boolean logic allowing an element to have degrees of true and false as opposed to being either 100% true or 100% false. Thus, it can account for the ‘shades of grey’ found in many real-world situations. In this paper, two fuzzy logic models are developed demonstrating its effectiveness as a model for vastly improving the current prioritisation system used in the UK and abroad. The first model converts an element of the current kidney transplant prioritisation system used in the UK into fuzzy logic. The result is an improvement to the current system and a demonstration of fuzzy logic as an effective decision-making approach. The second model offers an alternative prioritisation system to overcome the limitations of the current system both in the UK and abroad, as brought up by research reviewed in this paper. The current UK transplant prioritisation system, adapted in the first model, uses objective criteria (age of recipient, waiting time, etc) as inputs into the decision-making process. This alternative model takes advantage of the facility for infinitely varying inputs into fuzzy logic and a system is developed that can handle subjective (humanistic) criteria (pain level, quality of life, etc) that are key to arriving at such important decisions. Furthermore, the model is highly flexible allowing any number of criteria to be used and the individual characteristics of each criterion to be altered. The result is a model that utilises the scope of fuzzy logic's flexibility, usability and effectiveness in the field of decision-making and a transplant prioritisation method vastly superior to the original system, which is constrained by its use of only objective criteria. The ‘humanistic’ model demonstrates the ability of fuzzy logic to consider subjective and complex criteria. However, the criteria used are not intended to be exhaustive. It is simply a template to which medical professionals can apply limitless additional criteria. The model is produced as an alternative to any current national system. However, the model can also be used by individual hospitals to decide initially whether a patient should be placed on the transplant or surgery waiting list. The model can be further adapted and used for the transplant of other organs or similar decisions in medicine. Concurrently with the research and work carried out to develop the two models the investigation focused on the constraints of the current systems used in the UK and the US and the seemingly impossible dilemmas experienced by those having to make the prioritisation decisions. By removing the parameters of objective-only inputs the ‘humanistic’ model eradicates the previous limitations on decision-making.     

Fuzzy analytic hierarchy process: Fallacy of the popular methods

The fuzzy Analytic Hierarchy Process (fuzzy AHP) is a very popular decision making method and literally thousands of papers have been published about it. However, we find the basic logic of this approach has problems. From its methodology, the definition and operational rules of fuzzy numbers not only oppose the main logic of fuzzy set theory, but also oppose the basic principles of the AHP. In dealing with the outcomes, fuzzy AHP does not give a generally accepted method to rank fuzzy numbers and a way to check the validity of the results. Besides, we discuss the validity of the Analytic Hierarchy/Network Process (AHP/ANP) in complex and uncertain environments and find that fuzzy ANP is a false proposition because there is no fuzzy priority in the super matrix which provides the basis for the ANP. Although fuzzy AHP has been applied in many cases and cited hundreds of times, we hoped that those who use fuzzy AHP would understand the problems associated with this method.     

The specification of a reference direction using the analytic hierarchy process

In this paper we deal with the use of the Analytic Hierarchy Process (AHP) for specifying a reference direction, which is used to find a search direction in the visual interactive method developed by Korhonen and Laakso for multiple criteria problems. The reference direction describes how the decision maker would like to improve the values of multiple objectives and we show that the AHP is a convenient way to structure requisite preference information.     

Vendor rating for an entrepreneur development programme: a case study using the analytic hierarchy process method

With collaborative purchasing programmes where one of the aims is to develop suppliers, vendor rating is important not only in supplier selection and in deciding how to allocate business but also to determine where scarce development effort is best applied. This paper describes a case study into vendor rating for a government sponsored Entrepreneur Development programme in Malaysia. The paper reviews current methods for vendor rating and finds them wanting. It illustrates a new approach based on the use of Saaty's Analytic Hierarchy process method, which was developed to assist in multi-criteria decision problems. The new method overcomes the difficulties associated with the categorical and simple linear weighted average criteria ranking methods. It provides a more systematic way of deriving the weights to be used and for scoring the performance of vendors.     

The development of a hybrid intelligent maintenance optimisation system (HIMOS)

This paper reports on the development of a hybrid intelligent maintenance optimisation system (HIMOS) for decision support. It is a follow-up to an earlier paper published in the Journal of the Operational Research Society in 1995. Both papers refer to systems where there are very many components which may break down independently. When a component breaks down, corrective action (CO) is required. The problem is to determine the optimal maintenance policy, essentially the frequency of preventive maintenance (PM) which minimises the sum of down time due to PM and CO.HIMOS, like its predecessor IMOS, uses an ‘intelligent’ decision support system to carry out an automated analysis of the maintenance history data. Maintenance data are presented to the system and the most suitable mathematical model from a model-base is identified utilising a hybrid knowledge/case based system (KBS/CBR). Thus initially a rule base is applied to select a model, as in the case of IMOS. If no model is matched, the system reverts to its historical case-base to match the current case with a similar case that has been previously modelled. This double reasoning adds to the system's true learning capabilities (intelligence) and increases the rate of success of model selection. A prototype system is written in Visual Basic® for an IBM compatible PC. The study results include optimal PM intervals for a sample of industrial data sets. The results of the validation exercise of HIMOS against expert advice has shown that the system functions satisfactorily.     

Solving multiattribute design problems with analytic hierarchy process and conjoint analysis: An empirical comparison

We consider the multiattribute design problem (MADP) which contains a considerable number of alternatives, resulting from the combination of a limited number of discrete levels of several quantitative and/or qualitative attributes. In order to solve such problems, the preferences of individual decision makers have to be measured. Though a considerable number of methods is available from different research areas, only a subset is applicable to MADP.In this paper, we report on an empirical study which considered the problem of designing a university and involved more than 300 respondents. Because of this large-scale design, we performed a paper-and-pencil investigation and selected methods which could concisely be applied in such a setting: the analytic hierarchy process (AHP) and the conjoint analysis (CA).The results show that both methods give useful models of the respondents' preferences. However, inspecting the utility functions determined in detail reveals considerable discrepancies between them. Most of the measures used for comparison indicate AHP to be the better choice for the special decision situation considered. In order to get a more general recommendation, we categorize different types of MADP and discuss the applicability of AHP and CA.     

A comparative analysis of machine learning techniques and fuzzy analytic hierarchy process to determine the tacit knowledge criteria

Knowledge management is widely considered as a strategic tool to increase firm performance by enabling the reuse of organizational knowledge. Although many have studied knowledge management in a variety of business settings, the concept of tacit knowledge, especially the individual one, has not been explored in due detail. The objective of this study is to identify and prioritize individual tacit knowledge criteria and to explain their effects on firm performance. In the proposed methodology, first, the most prevalent individual tacit knowledge variables are identified by means of knowledge elicitation and feature selection methods. Then, the extracted variables were prioritized using machine learning methods and fuzzy Analytic Hierarchy Process (AHP). Support vector machine (SVM), logistic regression, and artificial neural networks are used as the first approach, followed by fuzzy AHP as the second approach. Based on the comparative analysis results, SVM (as the best-performed machine-learning technique) and fuzzy AHP methods were identified for the subsequent analysis. The results showed that both SVM and fuzzy AHP determined time efficiency of employees, communication between employees and supervisors, and innovative capability of employees as the most important tacit knowledge criteria. These findings are mostly supported by the extant literature, and collectively shows the synergistic nature of the utilized analytics approaches in determining individual tacit knowledge criteria.

     

On the evolution of an intelligent maintenance optimization system

In this paper the author reviews the development of an intelligent maintenance optimization system over the past 16 years. The paper starts with discussion of the initial motivation behind developing the system and the designs of the early versions of a computer program to access maintenance history data and provide an analysis. The concept behind this system was gradually developed to incorporate a rule base for the selection of a suitable model for preventive maintenance (PM) scheduling and then to a fully developed knowledge-based system for decision support. The need to incorporate case-based reasoning thus creating a hybrid system that can learn with use in addition to using elicited knowledge from experts is discussed. The experience with system validation with two versions of the system is analysed. The paper also reviews the extensive fundamental work on developing appropriate PM models that can deal with real data patterns. Finally, the scope for future development is presented.     

An analytic hierarchy process based approach to the design and evaluation of a marketing driven business and corporate strategy

Changes in the business environment suggest that to assure survival and growth in the 1990s and beyond, corporations must pay increased attention to creativity in generating strategic directions, rigor in the evaluation of strategic options on multiple and interdependent objectives, and vision and focus to assure effective utilization of resources. Here we present the conceptual structure of such an approach and review a number of recent applications.     

Clusters in a group: Decision making in the vector space formulation of the analytic hierarchy process

For a large group that might have a clustered structure, we discuss and implement an algorithm to group individuals into natural clusters using a convenient similarity measure. The cohesiveness of a homogeneous group or cluster is also investigated.     

A group decision making model based on goal programming with fuzzy hierarchy: an application to regional forest planning

In this paper a multi-criteria group decision making model is presented in which there is a heterogeneity among the decision makers due to their different expertise and/or their different level of political control. The relative importance of the decision makers in the group is handled in a soft manner using fuzzy relations. We suppose that each decision maker has his/her preferred solution, obtained by applying any of the techniques of distance-based multi-objective programming [compromise, goal programming (GP), goal programming with fuzzy hierarchy, etc.]. These solutions are used as aspiration levels in a group GP model in which the differences between the unwanted deviations are interpreted in terms of the degree of achievement of the relative importance amongst the group members. In this way, a group GP model with fuzzy hierarchy (Group-GPFH) is constructed. The solution for this model is proposed as a collective decision. To show the applicability of our proposal, a regional forest planning problem is addressed. The objective is to determine tree species composition in order to improve the values achieved by Pan-European indicators for sustainable forest management. This problem involves stakeholders with competing interests and different preference schemes for the aforementioned indicators. The application of our proposal to this problem allows us to be able to comfortably address all these issues. The results obtained are consistent with the preferences of each stakeholder and their hierarchy within the group.     

Geometry of decision making and the vector space formulation of the analytic hierarchy process

We extend the conventional Analytic Hierarchy Process (AHP) to an Euclidean vector space and develop formulations for aggregation of the alternative preferences with the criteria preferences. Relative priorities obtained from such a formulation are almost identical with the ones obtained using conventional AHP. Each decision is represented by a preference vector indicating the orientation of the decision maker's mind in the decision space spanned by the decision alternatives. This adds a geometric meaning to the decision making processes. We utilise the measure of similarity between any two decision makers and apply it for analysing decisions in a homogeneous group. We propose an aggregation scheme for calculating the group preference from individual preferences using a simple vector addition procedure that satisfies Pareto optimality condition. The results agree very well with the ones of conventional AHP.     

Delphic hierarchy process (DHP): A methodology for priority setting derived from the Delphi method and analytical hierarchy process

Identifying of criteria and objectives is of paramount importance to a decision-making process and is the basis for a sound decision. A systematic approach, therefore, is needed so that the objectives of the organization not only can be identified but also prioritized so that the resources will be allocated to the relative importance of the objectives and how well the alternatives satisfy them. The methodology proposed here uses the Delphi method and integrates it with the analytic hierarchy process. It assists the decision maker(s) to systematically identify the organizational objectives and then to set priorities among them. The application of the proposed model is illustrated by a case study.     

On the application of a model of condition-based maintenance

In this paper the modelling of condition monitoring information for three critical water pumps at a large soft-drinks manufacturing plant is described. The purpose of the model is to predict the distribution of the residual lifetimes of the individual pumps. This information is used to aid maintenance management decision-making, principally relating to overhaul. We describe a simple decision rule to determine whether maintenance action is necessary given monitoring information to date.     

Development of fuzzy state controller and its application to a hydraulic position servo

By combining control theory and fuzzy set theory, a new kind of state controller is proposed. Full order feedback and membership functions, which utilize the experience of experts, are used in the design of the state controller which we call a fuzzy state controller. Hydraulic position servos with a nonsymmetrical cylinder are commonly used in industry. This kind of system is nonlinear in nature and generally difficult to control. For different ending position, moving direction, strokes, and load the system dynamics is totally different. Once the above-mentioned parameters of the system are known, it is relatively straightforward to tune the gains of state controller to obtain good dynamic response. But when these parameters change, especially in case of the load, using the same gains will cause overshoot or even loss of system stability. Adaptive control is not applicable in this case due to the complexity of the algorithm, its rate of convergence, and the fast response characteristic of the system. The fuzzy state controller has been successfully applied to a hydraulic position servo. The system shows excellent robustness against variations of system parameters.     

Managing the tabu list length using a fuzzy inference system: an application to examination timetabling

In this paper, we present an application of Tabu Search (TS) to the examination timetabling problem. One of the drawbacks of this meta-heuristic is related to the need of tuning some parameter (like tabu tenure) whose value affects the performance of the algorithm. The importance of developing an automatic procedure is clear considering that most of the users of timetabling software, like academic staff, do not have the expertise to conduct such tuning. The goal of this paper is to present a method to automatically manage the memory in the TS using a Decision Expert System. More precisely a Fuzzy Inference Rule Based System (FIRBS) is implemented to handle the tabu tenure based on two concepts, “Frequency” and “Inactivity”. These concepts are related respectively with the number of times a move is introduced in the tabu list and the last time (in number of iterations) the move was attempted and prevented by the tabu status. Computational results show that the implemented FIRBS handles well the tuning of the tabu status duration improving, as well, the performance of Tabu Search.     

Symmetry-breaking bifurcation of analytic solutions to free boundary problems: An application to a model of tumor growth

In this paper we develop a general technique for establishing analyticity of solutions of partial differential equations which depend on a parameter . The technique is worked out primarily for a free boundary problem describing a model of a stationary tumor. We prove the existence of infinitely many branches of symmetry-breaking solutions which bifurcate from any given radially symmetric steady state; these asymmetric solutions are analytic jointly in the spatial variables and in .

     

An integrated model of statistical process control and condition-based maintenance for deteriorating systems

This paper proposes an integrated model of statistical process control and condition-based maintenance for a deteriorating system. We study a system that will not be as good as new after a preventive maintenance and can only survive a certain number of preventive maintenances. The system is modeled as a geometric process and monitored by an \(\bar{X}\) control chart. By analyzing the evolution of the system in different scenarios, we establish a mathematical model to minimize the expected cost during the expected cycle time that can be used to make an optimal replacement policy in applications. A computational scheme is presented and illustrated through a numerical example. A sensitivity analysis is performed to investigate the effect of statistical constraint, mean shift, and the parameters of the system.     

An interactive system for on-line management of the teaching process (Delta-U)

Translated from Issledovaniya po Prikladnoi Matematike, No. 8, pp. 115–117, 1980.